gammaFisherByHessian

Compute Fisher information matrix by the negative expected value of Hessian matrix in Gamma distribution.

Routines that allow the user to run goodness of fit tests based on empirical distribution functions for formal model evaluation in a general likelihood model. In addition, functions are provided to test a sample against Normal or Gamma distributions, validate the normality assumptions in a linear model, and examine the appropriateness of a Gamma distribution in generalized linear models with various link functions. Michael Arthur Stephens (1976) <http://www.jstor.org/stable/2958206>.

Payman Nickchi

gofedf

Goodness of Fit Tests Based on Empirical Distribution Functions

Richard Lockhart

gammaFisherByHessian function

<dl><dt>theta</dt>
<dd>a numeric vector of length two, containing MLE of parameters in Gamma dist</dd></dl>

Arguments

Compute Fisher information matrix by the negative expected value of Hessian matrix in Gamma distribution. — gammaFisherByHessian

<dl>

<dt>theta</dt>
<dd>a numeric vector of length two, containing MLE of parameters in Gamma dist</dd>

</dl>

gammaFisherByHessian: Compute Fisher information matrix by the negative expected value of Hessian matrix in Gamma distribution.

Description

Usage

Value

Arguments